Flow Error Estimation and Something about Oystercatcher

COST-STSM-IC0903-12676

Behavioral Classification of Oystercatchers byCombining Interactive Visualization and Machine LearningRudolf Netzel

1The classification of animal movement data is an important Basis for many physiological, evolutionary, energetic (etc.) inferencesLabels are only obtained during observationHuge amount of dataOnly few labeled dataCurrently statistical analyses dominate

Interactive visualization of spatio- temporal dataSupport labelingEvaluation of classification models

2Motivation

Oystercatcher [Wiki Media]22.04.2013 26.04.2013Follow-up cooperation due to results of the Dagstuhl seminarHost: Emiel van Loon, University of AmsterdamInstitute for Biodiversity and Ecosystem Dynamics

Ali Soleymani, University of ZrichSignal processing backgroundApply a segmentationModify feature vector with information form segmentation

Rudolf Netzel, University of StuttgartSimilarity to thesis Interactive LearningCreating a visualization for data labelingInteractive

3STSMGPSat non observation time~ every 10 - 30 min,at observation time ~ every 15s 60s

4DataobsID unique identifier per gps fixbirdID unique per birddate_time date-time stamp (UTC) of gps fixy, xcoordinate in m, Dutch coord. system RDnewspeed instantaneous speed (3D, in m/s)obsID unique identifier per gps fixindexindex per accelerometer observationx,y,z accelerationacceleration due to gravity (in g)Accelerometer

20 Hz over 3 seconds (up to 60 measures per gps fix)Used to derive model parameter

Interactive visualization of spatio- temporal dataSupport labelingEvaluation of classification models

Additional preferences of domain expertsEasy labelingPlots of arbitrary attributesParallel Coordinate2D Scatter PlotsComparison of multiple modelsLocation of frequent disagreement should be visibleDifferences in observed versus model behavior

5Framework RequirementsProject managementSpecification of color mappingsSelection / unselectionPlotsNavigationBrushing & Linking

Glyph to represent class labels6Framework Functionalities OverviewModel 1Model 2Model 3Observation labelGPS location mapRelabeling2D scatter plotParallel Coordinates7Framework - Component Overview

7Single and area selectionZoomingPanning

8Data Selection

Highlight object trajectoryN next points on an object trajectory9Data Selection Trajectory Mode

Label selected gps positionsUpdate of gps map10Labeling

Selection of arbitrary models and parameters for x und y axisColor encodes the object IDLines indicate a temporal correlationHighlighting of sub selected positions in gps map and Labeling View 112D Scatter Plot

Selection of arbitrary parameters form models Color encodes object ID

12Parallel Coordinates

13ConclusionBehavior classification is important for inferencesLarge difference between labeled and unlabeled dataRequirements of a framework that should support the labelingComponents of the framework so fareAdditional WorkClassifier retrainingWEKARun external java codeDisplay density or a map of gps fixes14Thanks for your attention!

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